A Supervised Classification Approach for Detecting Packets Originated in a HTTP-based Botnet

dc.creatorBrezo,Félix
dc.creatorde la Puerta,José Gaviria
dc.creatorUgarte-Pedrero,Xabier
dc.creatorSantos,Igor
dc.creatorBringas,Pablo G
dc.date2013-12-01
dc.date.accessioned2023-09-25T18:35:17Z
dc.date.available2023-09-25T18:35:17Z
dc.identifierhttp://www.scielo.edu.uy/scielo.php?script=sci_arttext&pid=S0717-50002013000300002
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8838461
dc.descriptionThe possibilities that the management of a vast amount of computers and/or networks offer is attracting an increasing number of malware writers. In this document, the authors propose a methodology thought to detect malicious botnet traffic, based on the analysis of the packets that flow within the network. This objective is achieved by means of the extraction of the static characteristics of packets, which are lately analysed using supervised machine learning techniques focused on traffic labelling so as to proactively face the huge volume of information nowadays filters work with.
dc.formattext/html
dc.languageen
dc.publisherCentro Latinoamericano de Estudios en Informática
dc.rightsinfo:eu-repo/semantics/openAccess
dc.sourceCLEI Electronic Journal v.16 n.3 2013
dc.subjectBotnet
dc.subjectDetection
dc.subjectMachine Learning
dc.subjectPackets
dc.subjectSupervised
dc.titleA Supervised Classification Approach for Detecting Packets Originated in a HTTP-based Botnet
dc.typeinfo:eu-repo/semantics/article


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